---
language: 
- sv-SE

license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
- cer
model-index:
- name: wav2vec2-xls-r-300m-swedish
  results:
  - task: 
      type: automatic-speech-recognition  # Required. Example: automatic-speech-recognition
      name: Speech Recognition  # Optional. Example: Speech Recognition
    dataset:
      type: mozilla-foundation/common_voice_8_0  # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
      name: common-voice # Required. Example: Common Voice zh-CN
      args: sv-SE       # Optional. Example: zh-CN
    metrics:
      - type: wer    # Required. Example: wer
        value: 38.57  # Required. Example: 20.90
        name: Test WER    # Optional. Example: Test WER
        args: 
        - learning_rate: 0.0003
        - train_batch_size: 64
        - eval_batch_size: 8
        - seed: 42
        - gradient_accumulation_steps: 2
        - total_train_batch_size: 128
        - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
        - lr_scheduler_type: linear
        - lr_scheduler_warmup_steps: 1000
        - num_epochs: 30
        - mixed_precision_training: Native AMP         # Optional. Example for BLEU: max_order
      - type: cer    # Required. Example: wer
        value: 10.98  # Required. Example: 20.90
        name: Test CER    # Optional. Example: Test WER
        args: 
        - learning_rate: 0.0003
        - train_batch_size: 64
        - eval_batch_size: 8
        - seed: 42
        - gradient_accumulation_steps: 2
        - total_train_batch_size: 128
        - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
        - lr_scheduler_type: linear
        - lr_scheduler_warmup_steps: 1000
        - num_epochs: 30
        - mixed_precision_training: Native AMP        # Optional. Example for BLEU: max_order
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# wav2vec2-large-xls-r-300m-Swedish

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4286
- Wer: 0.2729
- Cer: 0.0858


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 4.6203        | 5.49  | 500  | 2.8904          | 1.0    | 1.0    |
| 1.147         | 10.98 | 1000 | 0.5255          | 0.4107 | 0.1304 |
| 0.5246        | 16.48 | 1500 | 0.4598          | 0.3342 | 0.1058 |
| 0.378         | 21.97 | 2000 | 0.4316          | 0.2991 | 0.0949 |
| 0.298         | 27.47 | 2500 | 0.4286          | 0.2729 | 0.0858 |


### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0